1. Field of the Invention
This invention relates to a fraud score calculating program, which is effective in preventing a decrease in reliability due to the selection of inappropriate items in the calculation of a score using a model created based on Bayesian theory in the determination of fraud primarily in the use of credit cards and the like, a fraud score calculating method, and a fraud score calculating system for credit cards using the fraud score calculating program.
2. Description of the Related Art
Customarily, when a credit card is used, in order to prevent fraudulent transactions such as by a third party who has found the credit card and pretends to be the owner, the store or the like where the card is being used checks with the credit card company to ascertain the credit card balance as well as to conduct a credit inquiry concerning fraudulent use. In a system for such credit inquiry, it is becoming important to perform highly reliable determination using data on past fraudulent use and the like.
At present, credit card companies use a system which automatically determines a score for the possibility of fraudulent use on the basis of authorization data (data which is sent from the store or the like concerning the owner of the credit card, the monetary value of the transaction which is requested, etc.). In such systems, typically a score is determined by use of a scoring system which utilizes a neural network using neural theory (see Nonpatent Document 1).
A neural network is leading-edge technology which models the structure and information processing function of nerve cells of the human brain. Constructing such a system requires special know-how and a large monetary investment. Accordingly, many credit card companies do not themselves construct a basic system for score determination, but instead typically introduce a general purpose external system for portions relating to a neural network.
However, a scoring system using a neural network has problems, in that the logic for making a determination is a black box, so that the basis of determination is unclear to the credit card company or the like which utilizes it. In addition, as the user such as the credit card company does not itself create the neural network, difficulty is encountered in reflecting trends from the authorization data for that company. A conceivable measure for coping with such problems is to construct, in place of a neural network, a scoring system using a Bayesian network which uses Bayesian theory, which has recently come into use in the fields of artificial intelligence and the like. The basis of Bayesian theory is the probability of occurrence, which statistically predicts the probability of occurrence of a certain event.
Nonpatent Document 1
Asano Yoichiro, Suda Yoshinobu, “Introduction of a Fraudulent Use Detection System and Its Effects”, Gekkan Syohishashinyo, Kinzai Institute for Financial Affairs Research Group, May 2000, pages 16-19.
When it is attempted to determine fraudulent use of a credit card based on Bayesian theory, factors such as the time, the amount, the store, and the like are extracted from the manner of use of the credit card, they are classified into various cases based on combinations of these factors, and by calculating the probability that fraud occurred based on past authorization data for each case, a probability of occurrence can be determined. In order to calculate the probability of occurrence, past authorization data are collected, and a model which classifies the data by case is prepared. In this model, the data are classified into as many cases as possible, and by collecting a large amount of authorization data for each case, the reliability of the probability of occurrence can be increased.
However, if there are too many cases resulting from various combinations of factors, the samples which are used as parameters become too few, or it becomes easy for irregular cases to influence calculation, so there are situations in which cases arise having a low reliability of the probability of occurrence. Cases which should not be employed in calculating the score are preferably treated as so-called noise and removed from analysis.
Meanwhile, even when a special value is calculated, it is not appropriate to remove such a value as noise. In order to calculate a score which suppresses the effect of noise and has a higher reliability, it is necessary to select, for the authorization data which is to be evaluated, a case which corresponds to a combination of factors which includes the most reliable data.